Camera arrays, which may be provided on tablets or smartphones for example, may be provided to capture multiple images of the same scene except from different angles. These images can then be used to generate a 3D space or depth map, and accurately locate objects from the scene and into the 3D space. This is performed so that objects in the scene, or distances between objects in the scene (or from the camera to the object) can be measured, for computer vision, artificial intelligence, object recognition, and otherwise whenever it is desirable to know the size, position, or identity of an object in the scene. For example, one of the applications of 3D camera systems is measurement of distance between points of interest. This could be used to determine the size of furniture, determine the distance an object such as a person or vehicle traveled, or to determine a person's measurements such as their height, to name a few possible examples.
The depth information generated from the camera system is directly used to derive the 3D locations of the points of interest. The distance between these points may be calculated based on camera calibration information and the 3D locations. 3D camera systems can be broadly classified into two types: (1) based on active sensing such as laser scanners, or (2) passive image based systems such as stereo or multi-camera systems. The latter uses two or more images to estimate a disparity map or depth map for the scene that provides values of the disparity in point location from image to image. The estimated disparity is typically limited to integer values which corresponds to even pixel locations. As the distance to an object in a scene is farther from the camera (either to the object from the camera or the size of the object at a far distance), the distance measurement becomes more inaccurate because sub-pixel accuracy (between pixels or partial lengths between pixels) is not available.